System and method for integrated recommendations

ABSTRACT

A recommendation appliance, system and method are provided for generating and deploying additional web page content or functionality (e.g., retail recommendations) to an existing web page server system. For example, the present invention may be embodied as a reverse proxy server that is inserted as an intermediate network node between a web server and the end users accessing the web server. In this position, the recommendation appliance can introduce recommendation messages to web pages generated by the web server without requiring any modification to the code or architecture of the web server. In addition, the appliance may separately track the transaction activities of end users who receive recommendation messages and the transaction activities of end users who do not receive recommendation messages, so that a comparison of the effectiveness of the recommendations may efficiently be demonstrated without requiring any modification to the code or architecture of the web server.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computer networkcommunications. In one aspect, the present invention relates to anapparatus, system and method for selectively deploying web page content.

Description of the Related Art

The largest and best-known computer network in the world is the Internetwhich includes millions of computers that are used for commercial,academic, entertainment and government endeavors. With the advent ofgraphics-based Web browsers (such as Mosaic, Netscape Navigator, andInternet Explorer), the World Wide Web enabled users to access theexponentially growing content provided on the Internet. FIG. 1illustrates a conventional example of a communication session over theInternet whereby a user at a client computer 110 sends a request for aweb page over the Internet 120 that is serviced and returned from a webserver 130. When a user 110 begins a communication session over theInternet 120, the user can request data files from an Internet-connectedcomputer called a file server or website server 130. The website server130 accesses a web page content generator program 142 (shownillustratively as being stored in a database or memory 140) thatprovides data files, typically in website page format, that arerequested by the user. The web pages are typically written in a type ofprogramming code called Hypertext Mark-up Language (HTML) or ExtensibleMarkup Language (XML), and can be viewed or displayed through a browsercontaining a graphical user interface (GUI) program.

The design, assembly, creation and distribution of web page content 142or other Internet-based functionality at the web server site 130 can bea complex and expensive undertaking, requiring significant time andexpense to assemble a fully functional experience at the server 130. Ifit is ever necessary or desired that the web page content 142 or otherInternet-based functionality at the web server site 130 be enhanced,modified, extended or otherwise altered, the code or architecture at theserver 130 must be modified to include the additional content 144 as analteration to the code or architecture at the server 130. This processis often time-consuming and expensive.

The difficulty of making changes to the code or architecture at the webserver 130 also makes it difficult to demonstrate or test new featuresor functionality for the server 130. For example, the customer who ownsa web server 130 may be reluctant to investigate or test new web pagefeatures for the web site 130 where the cost, time, risk and effortrequired to modify the code at the server 130 are significant incomparison to the perceived advantages of the new web page features. Anadditional challenge is the cost, time, risk and effort required toremove the new feature from the code on the server 130 if the featuredemonstration or test is not successful.

To demonstrate the challenges presented by modifying a web site toinclude additional content 144, consider the example of addingrecommendation content or functionality to a retail website hosted atserver 130. In this example, the retail website server 130 was initiallydesigned to include a web page content generator 142 which receivesorder information from the user/client computer 110 and assembles a webpage which lists the purchased items contained in a cart for the user.If it is desired to add a purchase recommendation function to the webserver 130, this additional functionality can be difficult to addbecause of the complexity of the required programming and dataprocessing requirements. In particular, now that modern computers canassemble, record and analyze enormous amounts of data, historicaltransaction data can be collected and analyzed using data miningtechniques to generate purchase recommendations for the user to considerbased on discovering association relationships in a database byidentifying frequently occurring patterns in the database. Theseassociation relationships or rules may be applied to extract usefulinformation from large databases in a variety of fields, includingselective marketing, market analysis and management applications (suchas target marketing, customer relation management, market basketanalysis, cross selling, market segmentation), risk analysis andmanagement applications (such as forecasting, customer retention,improved underwriting, quality control, competitive analysis), frauddetection and management applications and other applications (such astext mining (news group, email, documents), stream data mining, webmining, DNA data analysis, etc.). Association rules have been applied tomodel and emulate consumer purchasing activities by describing how oftenitems are purchased together. Typically, a rule consists of twoconditions (e.g., antecedent and consequent) and is denoted as A C whereA is the antecedent and C is the consequent. For example, an associationrule, “laptop speaker (80%),” states that four out of five customersthat bought a laptop computer also bought speakers.

The difficulty of making recommendations increases as the number andcomplexity of mined association rules increases, which in turn is causedby an increase in the number of services and/or products, where eachservice or product may itself comprise a number of constituent servicesand products. The complexity of recommending a suitable configurationgrows further with the number of constituent parts, the external needsof the customer, and the internal needs of the parts when considered asa whole. As will be appreciated, the code required to implement such arecommendation functionality can also be extremely complex so that therecan be substantial time and expense required to add such functionalityto the web server 130 as additional content 144.

As seen from the conventional approaches, a need exists for methodsand/or apparatuses for improving the deployment of web page enhancementsthat can be quickly and easily integrated with existing web page serversystems. There is also a need for improved use, deployment,demonstration and/or testing of data mining techniques to generatepurchase recommendations for the end user while minimizing the need tochange the coding or architecture at the web server site. There is alsoa need to seamlessly generate highly granular frequent sets andrecommendations for use in an existing web server system withoutrequiring coding changes at the web server. Further limitations anddisadvantages of conventional systems will become apparent to one ofskill in the art after reviewing the remainder of the presentapplication with reference to the drawings and detailed descriptionwhich follow.

SUMMARY OF THE INVENTION

In accordance with one or more embodiments of the present invention, asystem and method are provided for generating and deploying additionalweb page content or functionality (e.g., retail recommendations) to anexisting web page server system. For example, the present invention maybe embodied as a recommendation appliance for introducing recommendationmessages in a web page server system. The appliance may include a portfor communicating with an end user machine that uses a web browser toissue web page requests and to display requested web pages. As will beappreciated, any type of port, including any communication connectionmethodology using wired or wireless techniques, can be used tocommunicate with the end user machine(s) and any web server machine(s).The appliance may also include a port for communicating with a webserver machine that generates requested web page content in response toa webpage request from the end user machine. The functionality of theappliance is implemented with a processing device and a memory thatstores instructions and data which cause the appliance to modify areceived web page, such as by inserting recommendation content orfunctionality into a requested web page before it is forwarded to theend user machine. When a web page request is received from an end usermachine, the appliance forwards the request through a port to a webserver machine identified in the web page request. In addition, theappliance uses data received from the end user machine (e.g.,recommendation context information associated with the end user) togenerate additional content or instructions for display to the end user(e.g., creating a purchase recommendation message) or to specify someother modification to be made to the requested web page. As a result,when the requested web page having original web page content is receivedat the appliance from the server machine, the appliance modifies theoriginal web page content (e.g., inserts the purchase recommendationmessage or otherwise deletes, inserts or modifies all or part of theoriginal web page content in real time), thereby generating a modifiedweb page, and then returns the modified web page through the first portto the first end user machine. Thus, the modified web page may includecontent that is static, passively interactive or interactive, in whichcase the appliance is configured and programmed to respond to anyresponse from the end user machine in response to the interactivecontent that was not included in the original web page content.

The appliance may also include instruction-based functionality forseparating a plurality of end user machines or web page requests intotwo or more groups, where the first group is to receive modified webpages and the second group is to receive requested web pages that arenot modified. The appliance may then separately record transactionactivities (e.g., purchase activity) of the first and second groups, sothat the transaction activities may be compared to demonstrate theeffectiveness of the web page modification.

As will be appreciated, the appliance can be implemented as a reverseproxy server that is operatively coupled in a variety of ways as anintermediate node in a web server system, such as by using a first portto couple the appliance to a load balancing server or other network nodeand using a second port to couple the appliance to a web server, to aload balancing server (which in turn is operatively coupled to aplurality of web servers), or to another network node.

The objects, advantages and other novel features of the presentinvention will be apparent from the following detailed description whenread in conjunction with the appended claims and attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a computer network, such as theInternet, over which web page requests by a user are conventionallyserviced and returned from a web server.

FIG. 2 illustrates various exemplary embodiments of a web page contentdeployment system and methodology whereby web page content and/orfunctionality is inserted in a requested web page.

FIG. 3 illustrates an exemplary recommendation appliance apparatus formodifying requested web pages by generating recommendation(s) using anexemplary computer parts domain knowledge base and tracking theperformance of the modified web pages.

FIG. 4 illustrates an exemplary data processing system and network forinserting recommendation content and/or other functionality into arequested web page.

FIG. 5 shows a flowchart schematically illustrating the processing ofweb page requests to include inserted content and/or functionality at anintermediate network node before returning the requested web page to theuser.

FIG. 6 illustrates a recommendation system, method and apparatus forgenerating and inserting modified content into a requested web page inaccordance with an alternative embodiment of the present invention.

DETAILED DESCRIPTION

An improved method and apparatus are described for generating anddeploying additional, altered or enhanced web page content orfunctionality (e.g., attribute-based retail recommendations) to anexisting web page server system. While various details are set forth inthe following description, it will be appreciated that the presentinvention may be practiced without these specific details. For example,selected aspects are shown in block diagram form, rather than in detail,in order to avoid obscuring the present invention. Some portions of thedetailed descriptions provided herein are presented in terms ofalgorithms or operations on data within a computer memory. Suchdescriptions and representations are used by those skilled in thecomputer network communications and data processing arts to describe andconvey the substance of their work to others skilled in the art. Ingeneral, an algorithm refers to a self-consistent sequence of stepsleading to a desired result, where a “step” refers to a manipulation ofphysical quantities which may, though need not necessarily, take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It is commonusage to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like. These and similar terms may beassociated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. Unless specificallystated otherwise as apparent from the following discussion, it isappreciated that throughout the description, discussions using termssuch as processing, computing, calculating, determining, displaying orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and/or transformsdata represented as physical, electronic and/or magnetic quantitieswithin the computer system's registers and memories into other datasimilarly represented as physical quantities within the computer systemmemories or registers or other such information storage, transmission ordisplay devices.

Referring now to FIG. 2, a block diagram illustrates various exemplaryembodiments of a web page content deployment system and methodologywhereby web page content and/or functionality is inserted in a requestedweb page. In a selected application, the deployment system 200 may beused in combination with a pre-existing web page system that includes aretail web server (e.g., 238) that uses a network (e.g., Internet 206)to deliver web page content (such as a user interface) to a usercomputer (such as a client computer 202). In addition to web-basedsystems, various embodiments of the present invention may be used todeploy content and/or functionality with other computer networkcommunication systems, such as any system where the user interface isdelivered to the user without requiring processing at the user computer,such as IBM 3270 “green screen” applications, text based applicationsaccessed via telnet, cell phones, mobile phones, PDAs and the like.

In the example of FIG. 2, a user interface or web page requested by theuser computer 202 from the retail web server 238 may be seamlesslyenhanced or modified by inserting or including a function/contentmodification server 230 between the user 202 and retail web server 238.Instead of directing the user's web page request directly to the retailweb server 238, the Internet 206 (e.g., a load balancer 204) may beconfigured to direct a web page request from the end user 202 to a firstport 229 of the content modification server 230 which processes datafrom the web page request while (or after) forwarding the web pagerequest through a second port 231 for servicing by the retail web server238. Of course a single port can be used to receive, forward, andretrieve data in the content modification server 230, or additionalports can be used. The web page request from the user 202 will containdata and/or will contribute to historical data for the user 202 that maybe processed by the content modification server 230 to determine if anyadditional content or functionality is to be included in the web pagereturned to the user. If the content modification server determines thatadditional content or functionality is to be included, then the contentmodification server 230 receives the web page content returned from theretail web server 238 and inserts the additional content orfunctionality into the returned web page content before forwarding themodified returned web page to the user 202. This is illustrated in FIG.2, where, in response to a web page request by the user 202, the retailweb server 238 generates web page content 242 that is stored in memory240 or is generated from a web page content generator program stored inmemory 240. Based on the requested web page content 242 returned fromthe retail web server 238, the content modification server 230 generatesmodified web page content 234 by inserting additional or modified webpage content and/or functionality 236. Again, the inserted web pagecontent/functionality 236 may be stored in memory 232 or may begenerated from a web page content generator program stored in memory232.

As will be appreciated, other network arrangements may be used to modifya user interface that is generated by a remote server in response to auser request. FIG. 2 illustrates additional illustrative embodimentswherein one or more user computers 201, 202 access one or more local webservers or applications 218, 224 over a computer network (intranet orinternet 206) using one or more load balancing servers 204, 216 and afunction or content modification server 208. At the function/contentmodification server 208, a user interface (e.g., content 222 from server218) may be modified or extended to insert additional content orfunctionality 214 before being forwarded to the end user (e.g., user201) who views the modified/extended content 212 (e.g., with a webbrowser). By positioning the content modification servers between afirst load balancer 204 and a second load balancer 216, web pagerequests from multiple users 201, 202 can be efficiently allocated tomultiple retail web servers 218, 224, and can be efficiently modified togenerate modified web page content 212 with inserted content orfunctionality 214 based on the original web page content 222, 228 fromthe retail web servers 218, 224. Of course, additional or fewer loadbalancing servers can be included as necessary, such as when a singlecontent modification server 230 is assigned to each retail web server238, or when a single content modification server 208 is connected tomultiple retail web servers 218, 224 through one or more load balancers216. In addition, the input load balancer 204 could be replaced with aserver 248 having combined load balancing and content modificationfunctions, in which case multiple retail web servers 244, 246 could bedirectly connected to the combined function server 248. Another networkarrangement is to connect a plurality of content modification servers inseries between the user computer(s) and the retail web server(s). Forexample, a first content modification server is connected to a secondcontent modification server which is then connected to a retail webserver. With such a series-connected network arrangement, a firstcontent modification server, instead of sitting in front of a webcontent server, sits in front of a second content modification server,which in turn sits in front of a web content server. Thus, the firstcontent modification server may be configured to add a purchaserecommendation to the original web page response from the retail webserver, while the second content modification server is configured toadd a selling point message to the original web page response from theretail web server. Other content and/or function modification can beintroduced with integrated, series-connected content modificationservers, including user interface modification functions.

In whatever network arrangement is used, the content modificationfunction may be advantageously implemented as a reverse proxy server toeffectively change the content or functionality of the user interfacefrom the server or application. For example, the reverse proxy server230 may be used to add content 236 (such as instructions for the user)to the content 242 from the local web server or application (e.g.,server 238) that is requested by one or more users 201, 202 over acomputer network. This content change in the user experience can beimplemented by the server 230 in a way that is seamless for the enduser(s) and without requiring any change or modification to the code orarchitecture of the local web server 238.

In operation, the user (e.g., 202) requests a web page by pointing itsweb browser to the reverse proxy (e.g., 230), or alternatively a networkadministrator configures the computer network 206 to automaticallydirect the user 202 to the content modification server 230 instead ofthe local web server or application (e.g., server 238). The contentmodification server 230 receives the request for the application fromthe user 202 and fetches the requested web page URL from the retail webserver 238 identified in the web page request. However, before thecontent 242 from the retail web server 238 is returned to the user 202,the content modification server 230 determines whether any additionalcontent or functionality is to be added to the requested application.For example, the content modification server may analyze data containedin the request from the user 202, or may analyze historical dataassociated with the user 202, or may analyze the web page contentreturned from the retail web server 238. Various examples of such dataanalysis are described in connection with the generation of purchaserecommendations based on attribute-based association rules, such asdescribed in the U.S. patent application Ser. No. 10/870,360 (entitled“Attribute Based Association Rule Mining” and filed Jun. 17, 2004), U.S.patent application Ser. No. 10/912,734 (entitled “System and Method forGenerating Effective Recommendations” and filed Aug. 5, 2004), U.S.patent application Ser. No. 10/912,743 (entitled “Retail RecommendationDomain Model” and filed Aug. 5, 2004), and U.S. patent application Ser.No. 10/912,699 (entitled “System and Method for Efficiently GeneratingAssociation Rules” and filed Aug. 5, 2004), each of which is assigned toTrilogy Development Group and is hereby incorporated by reference in itsentirety. Thus, the result of the data analysis may be the generation ofnew content 236 recommending one or more products for purchase by theuser 202 and the insertion of the new content 236 into the web pagecontent 234 that is to be returned to the user 202.

If content modification is required, the content stream 242 from theretail web server 238 may be transformed at the content modificationserver 230 by deleting, inserting or modifying the content stream inreal time before it is sent back to the user 202. In accordance withselected embodiments of the present invention, any new or modifiedcontent (referred to as instrumented content) 236 can be completelystatic, such as when additional textual information is inserted into theweb page. In addition or in the alternative, the new or modified content236 may include interactive content to provide interaction functionalityallowed by HTML form elements. In addition or in the alternative, thenew or modified content 236 may offer passive content interaction, suchas sending a message back to the content modification server when a usermoves a mouse icon over, out of or clicks on an HTML element.

As will be appreciated by persons of ordinary skill in the art, theability of the content modification server 230 to transform the originalweb page content 242 will require that the content modification serverbe programmed to “understand” the design, structure, layout and otherdisplay-related requirements of the web page content 242 generated bythe retail web server 238 and expected at the browser of the user 202.In this way, content modifications can be made without impairing theinformation contained it the original web page content 242. In addition,if interactive content 236 is added to the web page content 234 to bedisplayed to the user 202, the reverse proxy 230 may be configured tointercept this interaction which was not in the original application242, and thus would not be understood by the retail web server 238. In aselected embodiment, the content modification server 230 takesresponsibility for taking any action required as a result of receivingnotice of the user's interaction with the application having such aninteractive content.

Referring now to FIG. 3, a block diagram illustrates an exemplaryrecommendation appliance apparatus 300 for modifying requested web pagesby generating relevant and specific recommendation(s) using an exemplarycomputer parts domain knowledge base. As depicted, the recommendationsare inserted as additional content or functionality in the web pagesreturned to the web client machine(s) 313 by the retail web servermachine 370. To generate the recommendation content, one or more inputdata sets of transaction information (e.g., order history information,catalog information, customer order-related information and/or otherrecommendation context data 309) are mapped or otherwise transformedinto a second data set of transaction information (e.g., normalizedtransaction history, normalized catalog and/or normalized transactiondata) that provides more detailed information specifying the attributesof the purchased or recommended product(s). In a selected application,the second data set of transaction information may be used to generatecross-sell and up-sell recommendations based on frequent patterns minedfrom an order history stored in a transaction database. By providinggreater granularity to the transaction data, pattern correlation isimproved by representing items in terms of their features so that a partor product may be represented in terms of its part group and variousattribute-value pairs. For example, if there are three transactionsinvolving the purchase of a computer with the third transaction alsoinvolving the purchase of DVD disks, by including an identification foreach computer item of whether it includes a DVD drive (e.g.,Computer.DVDrive.No for the first and second computers andComputer.DVDrive.Yes for the third computer), sufficient detail isprovided to enable accurate correlation between the computer and diskpurchases when generating association rules. By transforming the inputdata set(s) to a shared or normalized representation, a common domainmodel or representation of the various input data sets is provided thatcan be used to generate one or more recommendations along with anassociated selling point message.

As depicted in FIG. 3, the recommendation appliance apparatus 300 isprovided for generating web page modifications consisting of prioritizedrecommendations and associated selling messages using an exemplarycomputer parts domain knowledge base. The recommendation applianceapparatus 300 is implemented as a central server computer system 311 anda memory or database 314 that are connected in a communication system(e.g., a private wide area network (WAN) or the Internet) between one ormore networked client or server computer systems 313 and one or morenetworked web servers 370. Communication between central server computersystem 311 and the networked computer systems 313, 370 typically occursover a network, such as a public switched telephone network overasynchronous digital subscriber line (ADSL) telephone lines orhigh-bandwidth trunks, for example, communications channels providing T1or OC3 service. Networked client computer systems (e.g., 313) typicallyaccess central server computer system 311 through a service provider(such as an internet service provider) or an access server 320 (such asa load balancing server) by executing application specific software,commonly referred to as a browser, on the networked client computersystems 313. By implementing the recommendation appliance apparatus 300as a reverse proxy server, the central server computer system 311 may beplaced in the communication link between the client(s) 313 and retailweb server 370 by making a simple address adjustment in the accessserver 320 and the retail web server 370.

In a selected example of how content modifications are generated, afirst data set of transaction information is stored in a memory ordatabase 314 that may be accessed directly or indirectly by the server311. In this example, the first data set identifies the items includedin one or more transactions (such as contained in the native orderhistory 351) by including a generic product descriptor 316, 318 for eachtransaction item, such as the SKU for a purchased product. Thus, a harddrive that was purchased is identified with the hard drive SKU 316 and adesktop computer is identified with the desktop SKU 318. In addition orin the alternative, the first data set of transaction informationidentifies normalized catalog data 353, customer order-relatedinformation, product identifiers for items purchased by the end userand/or other recommendation context data 309 associated with the user ata web client 313.

A transformation or normalizer process 305 maps or otherwise transformsthe first data set of transaction information into a second data set oftransaction information that provides more detailed informationidentifying with greater specificity the attributes of the specifiedproduct(s). As indicated, if only one of the input data sets (e.g., thecart data 309) requires normalization, then a single normalizer process305 is used, but if the order history data 351 or the native catalogdata (not shown) requires normalization, then separate instances of thenormalizer process 305 may be used. In a selected embodiment, the datatransformation is implemented with a computer or other data processingfunctionality (e.g., server 311) which loads a copy of the first dataset (e.g., 316, 318 from a database 314) into local memory (e.g., 315).As will be appreciated, the first data set may represent items from thecustomer cart 309, or may represent items from prior transactions in theorder history database 351. Using a product detail knowledge database(such as contained in the domain model schema 352) and/or the normalizedcatalog database 353 that specify various product feature details foreach transaction item, the server 311 invokes the normalizer process 305to map or transform the generic product descriptors of the first dataset (e.g., 316, 318) into a second data set (e.g., 354) that specifiesadditional details and/or features for the item of interest, such asmore detailed product descriptor information. In the depictedembodiment, part numbers in an order (e.g., 316, 318) may be mapped to apart group identifier and to a set of attribute names and values (e.g.,323-326, 327-334, respectively) and stored in the database 314. Forexample, an 80 Gb, 7200 RPM, SCSI drive identified with the HD-SKU 316could be mapped to the following numerical, attribute-based transactionitems:

_Hard_Drive

_Hard_Drive._Size. 80 Gb

_Hard_Drive._RPM. 7200

_Hard_Drive._Interface.SCSI

In accordance with the invention described herein, the transformation ofthe native cart or order history data may also use qualitative ortime-variant attribute values to represent the normalized data. Suchtime-variant attribute values can be useful where the relevancy ofrecommendations, whether automatically or manually generated, can expireover time as new technologies appear or new products are offered. Forexample, a processor that was the highest performing in its class sixmonths ago is no longer the highest performing in its class today. As aresult, the recommendations generated six months ago may no longer beuseful if the changes to the product offering and technology over timeare not taken into account. Manual recommendation updates are burdensomein requiring, at one extreme, replacement of outdated recommendationswith new, but similar, recommendations, and at the other extreme,modifying existing recommendations. To address this problem, thenormalizer process 305 may also include a transformation process foradjusting the values of attributes in the normalized data sets 353, 354,355 to account for the changing technologies and product offerings overtime. Products that were “high performance” or products that were“state-of-the-art” at the time the recommendations were originallygenerated may be adjusted to “moderate performance” or“middle-of-the-road technology.” For example, an 80 Gb, 7200 RPM, SCSIdrive identified with the HD-SKU 316 could be mapped to the followingqualitative, attribute-based transaction items:

_Hard_Drive

_Hard_Drive._Size. Medium

_Hard_Drive._RPM. Medium

_Hard_Drive._Interface.High

These items are included in a second data set 322 as an entry 323-326which quantifies the consumer preferences for one or more products andassociated product features and which is organized or stored in astructured format, such as a database or table. In this example, theoriginal item description 316 is now expanded and represented by aPartGroup identifier 323 and three attribute items 324, 325, 326. Insimilar fashion, the original item description 318 for a desktopcomputer is expanded and represented by a PartGroup identifier 327 andseven attribute items 328-334 (in this example) that are stored as anentry in the second data set 322. These additional attribute items328-334 specify the processor speed 328, processor class 329, operatingsystem type 330, hard drive size 331, optical drive type 332, softwarepackage type 333, and monitor type 334 for the desktop item.

Because of the additional product detail information contained in thesecond data set (e.g., normalized transaction history 354), it can beused in transaction database applications to provide more meaningfulfrequent pattern analysis that is performed by the analytics engine 306.A broad variety of efficient algorithms for mining association ruleshave been developed in recent years, including algorithms based on thelevel-wise Apriori framework, TreeProjection and FPGrowth techniques.For example, association rules may be generated from the expanded seconddata set of transaction information that is included as part of atransaction database 340 representing the normalized transaction history354, as indicated with entries 340 a-g in FIG. 3. An importantconsideration with data mining applications is the representation of thetransaction database 340. Conceptually, such a database can berepresented by a binary two-dimensional matrix in which every row (e.g.,340 a) represents an individual transaction (with a transactionidentifier (e.g., TID 100)) and the columns represent the items in thetransaction (e.g., f, a, c, d, g, l, m, p). Such a matrix can beimplemented in several ways. The most commonly used layout is thehorizontal data layout. That is, each transaction has a transactionidentifier and a list of items occurring in that transaction. Anothercommonly used layout is the vertical data layout, in which the databaseconsists of a set of items, each followed by its cover.

In the example of FIG. 3, the server 311 begins the process ofgenerating association rules 343 by retrieving the item descriptors fromthe normalized transaction database 340 and a minimum support count 348.The server 311 then identifies all items in the database 340 with afrequency meeting or exceeding the minimum support count requirement(e.g., the minimum support count is 3), and uses a rule generator 307(depending on the rule generation algorithm used) to generate aplurality of association rules 343 a-f. Each association rule mayinclude a support and confidence metric that is calculated by the server311. For example, the support metric 344 is determined by the number oftimes the rule is supported in the transaction database 340, and theconfidence metrics 345 are determined by the percentage of times theantecedent of the rule leads to the consequent.

Once the association rules 343 are finalized (which may optionallyinclude an optimization to remove redundant rules), the recommendationengine 308 is invoked to process the recommendation context informationgenerated by the cart 309 to identify potentially matching associationrules which are to be further processed for possible issuance asrecommendations 360 to be inserted in the requested web page returnedfrom the retail web server 370. To this end, a normalizer process 305may be executed to generate a normalized representation of therecommendation context 309 based on the domain model schema 352 and/ornormalized catalog 353, which may be stored as a normalized transactiondata set 355. The normalized transaction representation 355 may be usedby the recommendation engine 308 to identify association rules from themined rules 343 that match the recommendation context 309. For example,the recommendation engine 308 may include a rule evaluation process forevaluating the mined rules 343 against the normalized transactionrepresentation 355 (e.g., [f, c]) to generate a list of candidate ruleswhose trigger evaluated to true (e.g., R1, R2, R3) in response to therecommendation context 309. In addition, the recommendation engine 308may exclude any matching rules that do not meet a minimum confidencerequirement, leaving only high confidence rules in the candidate ruleset. The above-referenced Trilogy patent applications incorporatedherein by reference provide additional details concerning the processfor generating association rules, for processing, prioritizing, scoringand filtering candidate recommendation rules, and for generating sellingpoint messages to accompany the recommendation rules.

As a standalone or integrated server node, the recommendation applianceapparatus 300 depicted in FIG. 3 may also be used to track theperformance of the modified web pages in terms of the end useractivities taken in response to the content or functionality inserted inthe web pages. In particular, when an end user at a web client (e.g.,313) requests a web page from the retail web server 370, the request isreceived at the server 311 which invokes a fetch routine 301 to forwardthe web page request to the web server 370. After the web page requestis received, the server 311 may invoke an identification routine 302 todetermine if the requested web page should be enhanced, modified,extended or otherwise altered before being returned to the web client313. For example, the identification routine 302 may determine whether arecommendation message should be inserted in the requested web page byanalyzing data contained in the request from the user 313, or byanalyzing historical data associated with the user 313, or by analyzingthe web page content returned from the retail web server 370. When it isdetermined that a requested web page is to be modified, a modificationroutine 303 is invoked to insert the additional content and/orfunctionality (e.g., a purchase recommendation 360) into the returnedweb page using any desired web page editing techniques. As will beappreciated, the ability to transform the original web page content willrequire that the modification routine 303, alone or in combination withcustomer web page format information 361, be programmed to “understand”the design, structure, layout and other display-related requirements ofthe web page content generated by the retail web server 370 and expectedat the browser of the web client 313. The return routine 304 may beinvoked to return the modified requested web page to the end user 313 ina real time basis.

As an additional useful demonstration feature, the identificationroutine 302 may be used to segregate web page requests in apredetermined fashion for purposes of demonstrating the effectiveness ofthe added content and/or functionality provided by the recommendationappliance apparatus 300. The segregated requests may then be tracked inthe user history tracking database 362 to determine how the modifiedcontent affected the behavior of the end users. As but one example, apredetermined percentage of web clients submitting web page requests maybe filtered or separated out from the remaining web clients by theidentification routine 302 to receive purchase recommendations using anydesired predetermined recommendation technique (such as time-basedfiltering, end user identification-based filtering, etc.). Otherrequests may receive recommendations using a different recommendationtechnique or may receive no recommendation at all. By assemblingtransaction purchase data for all the clients in the user historytracking database 362 and comparing the purchase behavior of clientsthat received recommendations with that of clients that did not receiverecommendations (or that received different recommendations), theeffectiveness of the recommendation engine or different recommendationtechniques may be assessed. With this implementation of therecommendation appliance apparatus 300, the effectiveness of a contentmodification may be demonstrated without requiring any changes orrevisions to the coding or architecture of the retail web server 370. Inaddition, if the content modification is demonstrated to be ineffective,the content modification may be easily removed by simply removing therecommendation appliance apparatus 300 and connecting the retail accessserver 320 directly to the retail web server 370, again withoutrequiring any changes or revisions to the coding or architecture of theretail web server 370. Other useful selective modification examplesinclude presenting a different user interface to the end user.

In accordance with selected embodiments of the present invention, anintermediate computing or network node is used to add content orfunctionality to a user interface or to otherwise enhance, modify,extend or alter the user interface as it is being returned to therequestor. While present invention may be used to modify the userinterface in any desired manner, FIG. 4 illustrates an exemplary dataprocessing system and network 400 for inserting recommendation contentand/or other functionality into a requested web page. The dataprocessing system and network 400 includes one or more end usercomputers (e.g., client machine 402) and at least one web server 420.The data processing system and network 400 also includes a reverse proxyserver 408 in the communication path between the client machine 402 andthe web server 420, as determined by the load balancer 406. The reverseproxy server 408 is an example of an intermediate computing node thatprovides additional content or functionality to the system in a way thatis transparent to the client machine 402 and the web server 420.

In operation, a web page request from the client machine 402 istransmitted over a network (e.g., intranet/internet 404) and received bythe reverse proxy server 408, where it is forwarded to the web server420. Based on information contained in or associated with the web pagerequest and/or in response to programming or data contained in memory orstorage 422, the web server 420 generates a web page (e.g., web page424) and sends the generated web page 424 back to the user computer 402.However, the reverse proxy server 408 that is located in thecommunication path between the client machine 402 and the web server 420may modify the returned web page 424 to insert additional content and/orfunctionality, thereby producing a new or modified web page 426. Inparticular, simultaneously with or subsequent to forwarding of the webpage request, the reverse proxy server 408 determines if the requestedweb page 424 should be enhanced, modified, extended or otherwise alteredbefore being returned to the client machine 402. This determination canbe made in a variety of ways, including, but not limited to, using asimple look-up table techniques, using association rule miningtechniques to identify content or functionality that is associated withthe web page request or the requested web page, or to otherwisecorrelate additional content or functionality with data contained in orassociated with the web page request from the end user. In the exampledepicted in FIG. 4, the enhancements may include a list of recommendedproducts 414 a-d which correspond to the items 412 a-c listed in theshopping cart 412. Other examples of content or functionalityenhancements may also be provided by the reverse proxy server 408,including a customized message 416 to the end user, contextual helpinformation that is relevant to the user's browsing activity and thelike that is generated in response to the user's activity.

Turning now to FIG. 5, an exemplary flow methodology is illustrated forprocessing web page requests to include inserted content and/orfunctionality at an intermediate network node before returning therequested web page to the user. As will be appreciated, the methodologyillustrated in FIG. 5 shows how to process, modify and track web pagerequests. It will be appreciated by those of ordinary skill in the artthat the sequence of illustrated steps may be modified, reduced oraugmented in keeping with the disclosure of the present invention. Forexample, all user requests may be processed (thereby eliminating steps520, 522 and 524), or the user history tracking steps (524, 516) mayoptionally be included or excluded, or the user request analysis (step508) may be performed after the requested web page is received at theintermediate network node from the retail web server. Thus, it will beappreciated that the methodology of the present invention may be thoughtof as performing the identified sequence of steps in the order depictedin FIG. 5, though the steps may also be performed in parallel, in adifferent order, or as independent operations that are combined toobtain a score for the candidate recommendation rule.

The description of the method can begin at step 502, where theintermediate network node receives the user request from the clientmachine or end user. In a selected embodiment, the intermediate nodealso receives or accesses recommendation context data from the end userthat is associated with the user request. The mechanics of obtaining therecommendation context can be accomplished by a variety of ways, such asby retrieving quote information from the customer cart or by obtaininginformation from outside of a cart, such as from a simple product page,a configuration page, or even a user's click-path/browse-path/usersession history.

At step 501, the user request is forwarded by the intermediate networknode to the appropriate retail web server that is to service the userrequest. The forwarding step may be implemented with any desired networkaddressing techniques to deliver the user request to the web serverlocation specified in the user request or specified by an intermediateaddressing machine, such as a load balancing server.

At step 506, the intermediate network node determines if the userrequest is to be processed for purposes of adding content orfunctionality to the requested web page that has been requested by theuser. This determination allows different user requests or end useractivity to be treated differently, so that some requested web pages arenot modified (steps 520, 522), while others are (steps 508, 510, 512,514). The ability to differentiate the processing of user requestsbeneficially allows the intermediate network node to efficiently testand/or demonstrate the benefits or drawbacks of content or functionalitymodification provided by the intermediate network node.

For example, if the intermediate network node determines that a givenuser request is not to be processed (negative outcome from decision506), then the intermediate network node simply receives the requestedweb page from the server (step 520), forwards or returns the requestedweb page to the end user without modifying its content or functionality(step 522), and optionally updates the user history tracking database torecord this handling of the user request (step 524).

On the other hand, if the intermediate network node determines that agiven user request is to be processed (affirmative outcome from decision506), then the intermediate network node analyzes the user requestand/or its associated recommendation context data to determine if andhow the user requested web page is to be modified (step 508), receivesthe requested web page from the web server (step 510), and modifies thecontent or functionality of the requested web page (step 512) beforereturning the modified requested web page to the end user (step 514). Inaccordance with an illustrative embodiment, the intermediate networknode uses the recommendation context data from the user request toselect association rules that match the recommendation context, andthese selected association rules are used as a candidate recommendationrule set that is scored, prioritized and filtered to identify one ormore recommendation rules to be inserted in the requested web pagebefore its return to the end user. Once the recommendation rules areidentified, the intermediate network node inserts the recommendationrule text or functionality as web page content into the requested webpage and then forwards the modified web page to the end user. When arequested web page is modified and returned to the end user, theintermediate network node may optionally update the user historytracking database to record this handling of the user request (step516). With user history tracking data, the purchasing activity of endusers may be measured and compared to determine or demonstrate theeffectiveness of any particular web page modification undertaken at step512.

Persons skilled in the art will appreciate that a variety ofimplementation approaches may be used to modify web page content andfunctionality in accordance with the present invention. For example,FIG. 6 illustrates an alternative system, method and apparatus forgenerating and inserting modified content into a requested web page inaccordance with an alternative embodiment of the present invention. Asillustrated, a web browser on the client computer 602 is used tointeract with a target website, such as a retailer's web server 606,using a computer network that includes, for example, a DNS Server 604which is responsible for translating a domain name from a user requestinto a corresponding IP address. By implementing a recommendationappliance 610 as a reverse proxy server that is inserted between theuser 602 and the target website (e.g., retailer web server 606), theuser 602 that issues a web page request to the retailer web server 606can have web page responses modified at the recommendation appliance 610without the user 602 being able to detect that it is not interactingdirectly with the target website 606, and without modifying the code orarchitecture of the retailer web server 606. In a selected embodiment,this may be accomplished by providing a response interpreter 614 on therecommendation appliance 610 to extract data from the response generatedby the retailer web server 606. The response interpreter 614 issynchronized with the recommendation generator 616 to extract data fromthe web page response(s) that the recommendation generator needs toinsert dynamic content into the response from the target website 606. Ofcourse, the response interpreter can be synchronized with any type ofcontent modification functionality (and not just a recommendationgenerator) that examines the web page response, takes some action, andthe inserts or revises the response based on that action.

The operational flow of the system, method and apparatus depicted inFIG. 6 may be understood with reference to a user that requests a webpage (communication A) from a retailer (www.retailer.com) using a webbrowser on the client machine 602. At the DNS server 604, the DNS entryfor www.retailer.com is configured to point to the IP address of themachine (or load balancer for) the recommendation appliance 610, insteadof pointing directly to the IP address of the load balancer for theretailer web server(s) 606. For purposes of this illustration, theretailer web server 606 corresponds to a DNS entry ofwww.old.retailer.com. To route the web page request to therecommendation appliance 610, the DNS server 604 returns the updated IPaddress to the user 602 (communication B). The user's web browser maythen connect to the recommendation appliance 610 to request the URL forthe requested web page (communication C).

The reverse proxy module 612 in the recommendation appliance 610translates the web page request of the formhttp://www.retailer.com/product/info.html tohttp://oldsetailer.com/product/info.html, and makes a request to the newURL at the retailer web server 606 (communication D). All otherinformation about the web page request from the user machine 602 (e.g.,cookies and post data) is unchanged.

When the reverse proxy 612 receives the response from old.retailer.com(communication E), the response includes whatever original content wasgenerated by the retailer web server 606 in response to the web pagerequest. For example, a web page 620 may be generated that includes alisting of product information as the original content in response tothe web page request seeking product information from the retailer webserver 606.

The reverse proxy 612 forwards the original response from the retailerweb server 606 to a response interpreter 614 (communication F) whichextracts the information needed to determine if or how the originalresponse is to be changed or modified. For example, the responseinterpreter 614 extracts the information or data from the originalresponse that the recommendation generator 616 will expect. Theextracted information or data is returned by the response interpreter614 to the reverse proxy 612 (communication G), which then submits thisinformation or data as a request to the recommendation generator module616 (communication H). A response from the recommendation generator 616(such as a listing of product purchase recommendations) is returned tothe reverse proxy 612 (communication I). While the response from therecommendation generator 616 may already be formatted for inclusion inthe requested web page, it may alternatively be separately submitted toa format and placement module 618 (communication J) for HTML formatting,with specific instructions about the placement position within the webpage response 620 from the retailer web server 606 being returned to thereverse proxy 612 (communication K). The use of a separate format andplacement module 618 allows customized formatting for each targetretailer web site, as indicated by the depiction of multiple instancesof the format and placement module 618 in FIG. 6. However the contentmodification is formatted, the fully formatted HTML response 624 maythen be returned to the user (communication L), where the response 624includes the modified content (such as the original listing of productinformation modified to include additional recommendations generated bythe recommendation generator 616).

In an exemplary embodiment, the system and method for generating anddeploying additional web page content or functionality (e.g., retailrecommendations) to an existing web page server system may beimplemented with a standalone data processing system (such as arecommendation appliance) that modifies the web page content returned toan end user (such as by processing transaction database information toprovide attribute-based association rules that are scored andprioritized to generate purchase recommendations with associated sellingpoint/message texts). Various examples of standalone data processingsystems for generating purchase recommendations with associated sellingpoint/message texts are described in U.S. patent application Ser. No.10/912,734 (entitled “System and Method for Generating EffectiveRecommendations” and filed Aug. 5, 2004) and U.S. patent applicationSer. No. 10/912,743 (entitled “Retail Recommendation Domain Model” andfiled Aug. 5, 2004), each of which is assigned to Trilogy DevelopmentGroup and is hereby incorporated by reference in its entirety. Forexample, data processing may be performed on computer system 300 whichmay be found in many forms including, for example, mainframes,minicomputers, workstations, servers, personal computers, internetterminals, notebooks, wireless or mobile computing devices (includingpersonal digital assistants), embedded systems and other informationhandling systems, which are designed to provide computing power to oneor more users, either locally or remotely. A computer system 300includes one or more microprocessor or central processing units (CPU)312, mass storage memory 314 and local RAM memory 315. The processor312, in one embodiment, is a 32-bit or 64-bit microprocessormanufactured by Motorola, such as the 680X0 processor or microprocessormanufactured by Intel, such as the 80X86, or Pentium processor, or IBM.However, any other suitable single or multiple microprocessors ormicrocomputers may be utilized. Computer programs and data are generallystored as instructions and data in mass storage 314 until loaded intomain memory 315 for execution. Main memory 315 may be comprised ofdynamic random access memory (DRAM). As will be appreciated by thoseskilled in the art, the CPU 312 may be connected directly (or through aninterface or bus) to a variety of peripheral and system components, suchas a hard disk drive, cache memory, traditional I/O devices (such asdisplay monitors, mouse-type input devices, floppy disk drives, speakersystems, keyboards, hard drive, CD-ROM drive, modems, printers), networkinterfaces, terminal devices, televisions, sound devices, voicerecognition devices, electronic pen devices, and mass storage devicessuch as tape drives, hard disks, compact disk (“CD”) drives, digitalversatile disk (“DVD”) drives, and magneto-optical drives. Theperipheral devices usually communicate with the processor over one ormore buses and/or bridges. Thus, persons of ordinary skill in the artwill recognize that the foregoing components and devices are used asexamples for the sake of conceptual clarity and that variousconfiguration modifications are common.

The above-discussed embodiments include software that performs certaintasks. The software discussed herein may include script, batch, or otherexecutable files. The software may be stored on a machine-readable orcomputer-readable storage medium, and is otherwise available to directthe operation of the computer system as described herein and claimedbelow. In one embodiment, the software uses a local or database memoryto implement the data transformation and data structures so as toimprove the generation of attribute-based rules and purchaserecommendations. The local or database memory used for storing firmwareor hardware modules in accordance with an embodiment of the inventionmay also include a semiconductor-based memory, which may be permanently,removably or remotely coupled to a microprocessor system. Other new andvarious types of computer-readable storage media may be used to storethe modules discussed herein. Additionally, those skilled in the artwill recognize that the separation of functionality into modules is forillustrative purposes. Alternative embodiments may merge thefunctionality of multiple software modules into a single module or mayimpose an alternate decomposition of functionality of modules. Forexample, a software module for calling sub-modules may be decomposed sothat each sub-module performs its function and passes control directlyto another sub-module.

The computer-based data processing system described above is forpurposes of example only, and may be implemented in any type of computersystem or programming or processing environment, or in a computerprogram, alone or in conjunction with hardware. The present inventionmay also be implemented in software stored on a computer-readable mediumand executed as a computer program on a general purpose or specialpurpose computer. For clarity, only those aspects of the system germaneto the invention are described, and product details well known in theart are omitted. For the same reason, the computer hardware is notdescribed in further detail. It should thus be understood that theinvention is not limited to any specific computer language, program, orcomputer. It is further contemplated that the present invention may berun on a standalone computer system, or may be run from a servercomputer system that can be accessed by a plurality of client computersystems interconnected over an intranet network, or that is accessibleto clients over the Internet. In addition, many embodiments of thepresent invention have application to a wide range of industriesincluding the following: retail, enterprise and consumer electronics,general retailers, computer hardware and software manufacturing andsales, professional services, financial services, automotive sales andmanufacturing, telecommunications sales and manufacturing, medical andpharmaceutical sales and manufacturing, and construction industries.

Although the present invention has been described in detail, it is notintended to limit the invention to the particular form set forth, but onthe contrary, is intended to cover such alternatives, modifications andequivalents as may be included within the spirit and scope of theinvention as defined by the appended claims so that those skilled in theart should understand that they can make various changes, substitutionsand alterations without departing from the spirit and scope of theinvention in its broadest form.

What is claimed is:
 1. A method for introducing recommendation messagesin a web page server system, comprising: executing instructions, storedin a memory, by one or more processors of a data processing system toperform: communicating with at least one end user computer thatcomprises a web browser for issuing web page requests and displayingrequested web pages; communicating with at least one web server computerthat generates requested web page content in response to a web pagerequest; forwarding a web page request received from a first end usercomputer to a first web server computer identified in the web pagerequest; receiving a requested web page comprising original web pagecontent from the first web server computer that is generated in responseto the web page request from the first end user computer; generating atleast one purchase recommendation based on a recommendation contextassociated with the first end user computer that may be extracted fromthe web page request or from the requested web page received from thefirst web server computer; modifying the original web page content toinclude the purchase recommendation, thereby generating a modified webpage; and returning the modified web page to the first end usercomputer.
 2. The method of claim 1 further comprising executing theinstructions by the one or more processors of the data processing systemto perform: determining a load balanced communication path for areceived web page request across one or more communication paths to oneor more computing resources; and wherein forwarding a web page requestfurther comprises: forwarding a web page request received from a firstend user computer over an identified load balanced communication path toa first web server computer identified in the web page request.
 3. Themethod of claim 2 further comprising executing the instructions by theone or more processors of the data processing system to perform:determining the load balanced communication path, and one or morecontent modification servers for generating at least one purchaserecommendation.
 4. The method of claim 2 further comprising executingthe instructions by the one or more processors of the data processingsystem to perform: determining the load balanced communication path, anda plurality of content modification servers connected in series forgenerating at least one purchase recommendation.
 5. The method of claim1 further comprising executing the instructions by the one or moreprocessors of the data processing system to perform: modifying the webpage requests to adapt any part of the web pages requested thatcorresponds to previously modified functionality or content such that aweb server identified in the web page requests can interpret the webpage requests.
 6. The method of claim 1 further comprising executing theinstructions by the one or more processors of the data processing systemto perform: modifying the web page requests to adapt any part of the webpages requested that corresponds to a purchase recommendation includedin a previously returned modified web page so that the web serveridentified in the web page requests can interpret the web page requests.7. The method of claim 1 wherein the recommendation context comprisesorder history information associated with a user who issued the web pagerequest.
 8. The method of claim 1 wherein the recommendation context isselected from the group consisting of current user activity of thecustomer at the client computer, past activity of other users who havesubmitted web page requests, order history information, cataloginformation, and customer order-related information.
 9. The method ofclaim 1 wherein modifying the original web page content comprisesconfiguring the modified web page content to display a plurality ofpurchase recommendations in order of relevance priority.
 10. The methodof claim 1 wherein modifying the original web page content comprisesconfiguring the modified web page content to display a plurality ofpurchase recommendations using qualitative attribute values.
 11. Themethod of claim 1 wherein modifying the original web page contentcomprises configuring the modified web page content to display aplurality of purchase recommendations using time-variant attributevalues.
 12. The method of claim 1 wherein generating at least onepurchase recommendation comprises selecting from among a plurality ofassociation rules to identify a plurality of candidate recommendationshaving a trigger that matches the recommendation context associated withthe web page request.